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These ten AI prompts can help draft follow-ups and quotes, sort CRM records, prepare qualification decisions, and turn meetings into decision logs. Their useful boundary is just as important: they work from the material you provide, flag missing information instead of inventing it, and leave consequential actions—such as sending, deleting, merging, or approving a price—to a person.
The examples below are adapted from sales and operations expert Archit Mittal’s August 15, 2026 article. They are prompt-design examples, not evidence of measured savings, improved conversion, or a particular accuracy rate.
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How to use these prompts safely
Give the model the source material the task depends on: conversation notes, a rate card, a lead export, customer replies, reference documents, or a meeting transcript. Tell it what output to produce and what to do when the evidence is incomplete. A prompt cannot recover information that was never supplied, so missing details should remain questions or unknowns rather than plausible guesses.
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Use the output as a draft or recommendation, not as authorization to act. Mittal summarizes the principle this way: “Every one of these prompts drafts, reports or sorts. None of them send.” A slightly wrong date, discount, or confidence label can matter when it is sent under your name or used to change a business record.
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1. Draft a three-message follow-up
What to provide
Supply notes or a transcript from the last business-to-business sales conversation, including any genuine development since then. The person using the prompt—not the model—must provide what has changed.
Prompt
Using only the conversation notes below, draft three short WhatsApp follow-ups. Do not invent facts or urgency. Message 1 must include new information; if there is none, return exactly “NO NEW INFORMATION — do not send.” Message 2 should give a real reason to decide, if the notes support one. Message 3 should close the loop politely, without guilt or another pitch. Label the messages 1, 2, and 3. Notes: [paste notes]Check every claim and send only the message that fits the situation. If there is no genuinely new information for the first message, the specified stop signal is the output—not a cue to make something up.
2. Prepare a quotation from a rate card
What to provide
Include the customer’s enquiry and the current rate card. If the rate card does not cover a requested service or leaves a pricing question unanswered, the model should flag it rather than improvise a price.
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Prompt
Draft a concise quotation using only the enquiry and rate card below. Organize it into: understood need, inclusions, exclusions, price, and validity period. Use only prices stated in the rate card. Flag any requested scope the rate card does not cover, and list unanswered pricing questions instead of guessing. Do not approve or send the quote. Enquiry: [paste enquiry] Rate card: [paste rate card]A person should confirm the scope, price, exclusions, and validity period before the quote becomes a commitment.
3. Triage dormant CRM leads
What to provide
Provide a CSV export or its rows, explain the column names, and state what counts as dormant or actionable for your business. Without those definitions and adequate record details, the model has little basis for classifying a lead.
Prompt
Review the lead records and column definitions below. Assign each record exactly one label: REVIVE, PARK, DELETE, or UNKNOWN, followed by a one-line reason based only on the supplied fields. Use UNKNOWN when the record is too sparse to judge. Draft a follow-up message only for REVIVE records. Treat DELETE as a suggestion for human review; do not delete anything. Column definitions: [paste definitions] Records: [paste export]Review proposed deletions against your retention rules and business records. A label alone does not establish that a record is safe to remove.
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4. Qualify a prospect before booking a call
Gather the four details first
Ask the prospect for their desired outcome, what they have already tried, their timing, and their budget range. The recommendation is only as useful as the answers; an unanswered budget or timeline should not be silently filled in.
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Based only on the prospect's replies below, recommend BOOK, NURTURE, or DECLINE. Give a brief reason tied to the replies, then draft a response. If recommending DECLINE, make it clear and warm, and suggest another course of action only if appropriate. Do not infer missing answers. Replies: [paste replies]Read decline messages before sending them. A recommendation can help organize a decision, but it does not know every commercial or relationship consideration behind it.
5. Analyze a lost deal for the record
Prompt
Analyze the lost-deal notes below. Return: (1) the reason explicitly stated, (2) a possible underlying reason only if supported by the notes, (3) where in the process the deal appears to have turned, and (4) what would have been useful to know earlier. Separate stated facts from interpretations; do not present an inference as confirmed. Notes: [paste notes]This is an interpretation of the account you provide, not independently verified knowledge of why the buyer made the decision. It does not itself contact the customer.
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6. Make a retrieval assistant expose gaps
Prompt
Answer only from the documents provided. If the answer is not in them, reply exactly: “I don't have that — let me check and come back.” Never infer, estimate, or fill a gap from general knowledge. Quote the line you used.Supply the relevant documents with the question and check the quoted line against them. This instruction is intended to make an information gap visible; it does not guarantee that an AI system will never produce an unsupported answer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Audit decisions before automating them
Prompt
For each team decision below, assess how reversible it is and classify it as AUTOMATE, AUTOMATE WITH HUMAN APPROVAL, or NEVER AUTOMATE. Explain the reason briefly. Treat money leaving the business, filings, and messages sent under a person's name as NEVER AUTOMATE unless explicit authorization says otherwise. Decisions: [list decisions]Use the review to identify where a person must remain in the process. The source’s default is to keep outgoing money, filings, and messages in a person’s hands unless there is explicit authorization to do otherwise.
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Prompt
For each recurring report below, identify the decision that changes because someone receives it. Label it DELETE if no decision can be named, REPLACE if one number would suffice, or KEEP otherwise. Give a short reason. Reports and recipients: [list reports and recipients]Ask report recipients before removing anything. A report may be checked as a reference even when it does not lead to a decision that appears in a meeting.
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9. Find possible customer-record duplicates without merging
Prompt
Review the customer-list export for possible spelling, abbreviation, phone-number, or city variants. Group possible matches and give each group a confidence level with the fields that support it. Put records that cannot be judged in a separate list. Report possible matches only: do not merge, delete, or edit any records. Export: [paste export]Treat groups as candidates for human verification, not confirmed duplicates. Similar names or locations can belong to different people or businesses, and the prompt should not alter the underlying records.
10. Turn a meeting transcript into a decision log
Prompt
From the transcript below, list only decisions that were actually made. For each, include the owner and due date if stated. Then list what remains open. Omit discussion that did not reach a decision. If no decision was made, say so instead of summarizing the whole conversation. Transcript: [paste transcript]Check owners and dates against the transcript before circulating the log. If they were not stated, the output should not manufacture them.
What these examples can—and cannot—show
The prompts illustrate a practical design pattern: supply source material, constrain the requested format, specify how to handle missing evidence, and identify where human review or approval belongs. They do not establish that using the prompts increases revenue, improves conversion, saves a set amount of time, or produces a measured level of accuracy. The examples are practical guidance from their author, not an independently tested productivity study.
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